A medical image diagnosis apparatus is used in medical institutions and the like to obtain information on tissues in a subject. The medical image diagnosis apparatus creates from the information a medical image such as, for example, fluoroscopic image, tomographic image, and blood flow image. The medical image is used for examination and diagnosis.
Examples of the medical image diagnosis apparatus include X-ray CT (computed tomography) systems, MRI (magnetic resonance imaging) equipment, ultrasound diagnosis apparatuses, and X-ray diagnosis apparatuses. Detection data obtained by such variety of medical image diagnosis apparatuses are subjected to various types of image processing to generate medical images. Image processing may be used to visualize a blood flow, a flow of a contrast agent, and the like in a medical image. Image processing may also be used to extract a lesion, contour of internal organs, and the like.
Examples of the image processing include noise removal or reduction, feature extraction, and pattern recognition. These are used alone or in combination as appropriate. By a technique to reduce random noise in an image, a predetermined region or site in a subject's body is clearly represented in the image.
Besides, the medical image diagnosis apparatus may obtain information on the two-dimensional region of an object, or it may obtain information on the three-dimensional region of an object. Plane data or volume data is generated based on the information on the two-dimensional region or the three-dimensional region thus obtained. The plane data is formed of a two-dimensional array of pixels, while the volume data is formed of a three-dimensional array of voxels (pixels). Each pixel or voxel of the plane data or the volume data is assigned with information (pixel value, etc.) indicating the density or the like of the object in the region.
Smoothing is known as a conventional noise reduction technique. The noise reduction for plane data is explained below. The smoothing refers to processing in which, with respect to an input value f (i, j) of a pixel (i, j), the average density of peripheral pixels around the pixel (i, j) is used as an output value g (i, j). Assuming that n×n pixels in the vicinity of the pixel (i, j) are used as the peripheral pixels, the output value g (i, j) is obtained by the following formula (1):
                              g          ⁡                      (                          i              ,              j                        )                          =                              ∑                          k              =              a                        b                    ⁢                                    ∑                              m                =                c                            d                        ⁢                                          1                                                      (                                          b                      -                      a                      +                      1                                        )                                    ⁢                                      (                                          d                      -                      c                      +                      1                                        )                                                              ·                              f                ⁡                                  (                                                            i                      +                      k                                        ,                                          j                      +                      m                                                        )                                                                                        (        1        )            where a, b, c, and d indicate the coordinates of the peripheral pixels to be averaged and are integers, and 1/(b−a+1)(d−c+1) is the total number of the peripheral pixels.
However, the simple use of noise reduction may create a so-called “edge blur”. The edge blur reduces the spatial resolution of an image, and the whole image is blurred. When the above noise reduction using formula (1) is applied to a medical image of a detailed blood vessel structure, pixels other than those of the blood vessel structure are also averaged (smoothed). That is, even if reducing noise, the smoothing also reduces contrast representing the blood vessel structure. As a result, the visualization of the blood vessel structure may be difficult in the medical image that is supposed to illustrate the detailed blood vessel structure.
For this reason, in addition to noise reduction in which peripheral pixels are simply averaged as described above, a weighted averaging may also be performed by weighting the peripheral pixels based on the similarity of them. In this case, an image processing apparatus calculates the similarity of a pixel and peripheral pixels around the pixel, and obtains a weighted average of them according to the similarity. The image processing apparatus uses the weighted average as a pixel value of the pixel. Thus, the image processing apparatus is capable of reducing image blur as well as reducing noise.
Further, the image processing apparatus diffuses pixel information to reduce noise. On this occasion, the degree of diffusion is determined according to whether edge information contains a pixel of interest to prevent edge blur. This processing is called anisotropic diffusion filtering.
Still further, the image processing apparatus performs noise reduction on volume data. For example, the image processing apparatus performs three-dimensional noise reduction on the volume data, or noise reduction (two-dimensional noise reduction) for the arbitrary cross-section of the volume data.